Image super-resolution rebuilding method based on confidence coefficient kernel regression

A technology of super-resolution reconstruction and confidence, which is applied in the field of image processing, can solve the problems of unsatisfactory suppression and unsatisfactory results, and achieve the effect of increasing the reliability of pixels, suppressing outliers, and strong robustness
CN103971335AInactive Publication Date: 2014-08-06HOHAI UNIV
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Patent Information

Authority / Receiving Office
CN · China
Current Assignee / Owner
HOHAI UNIV
Publication Date
2014-08-06
Estimated Expiration
Not applicable · inactive patent

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Abstract

The invention provides an image super-resolution rebuilding method based on the confidence coefficient kernel regression. According to the method, a confidence coefficient kernel function is constructed, the confidence coefficient kernel regression is carried out on an image through the confidence coefficient kernel function, interpolation in the image super resolution rebuilding is finished, by the adoption of the confidence coefficient kernel regression, not only is a double weighted mechanism of the self-adaption kernel regression inherited, but also distinguishing of pixel reliability is added, the abnormal value can be better restrained, and the image super-resolution rebuilding is achieved. According to the method, the image super-resolution rebuilding is achieved, the robustness is high, and the method can be widely applied to the fields such as monitoring, remote sensing, military, medical science and video entertainment and has the important value.
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Description

technical field

[0001] The invention belongs to the technical field of image processing, and specifically refers to an image super-resolution reconstruction method based on confidence kernel regression. Background technique

[0002] With the advent of the information age, digital imaging technology and related applications have achieved rapid development. Many CCD (charge-coupled device) and CMOS image sensors are widely used to acquire digital images. Although these sensors can meet most imaging applications, their resolution level and cost are far from meeting people's consumption needs, especially the needs of high-end technology research and development. For example, in digital imaging applications such as monitoring, remote sensing, military, medical and video entertainment, high-quality images require not only sufficient pixel density but also rich detail information. Due to the influence of technological level, cost and other factors, it is often not realistic to re...

Claims

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